# dformoso/machine-learning-mindmap

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6,254 stars · 1,010 forks · Apache-2.0

## Links

- GitHub: https://github.com/dformoso/machine-learning-mindmap
- awesome-repositories: https://awesome-repositories.com/repository/dformoso-machine-learning-mindmap.md

## Description

This project is a machine learning knowledge map and educational resource that provides a structured learning path for data science. It organizes core concepts, from basic data analysis to deep learning, into a visual guide and markdown-based knowledge graph.

The resource connects theoretical foundations and mathematical concepts to practical execution through links to runnable notebooks and implementation examples. This allows for a transition from conceptual study to hands-on practice.

The project uses hierarchical node organization and modular topic decomposition to visualize relationships between technical topics. This structured data is delivered via a static site interface for accessibility.

## Tags

### Part of an Awesome List

- [Data Science Learning](https://awesome-repositories.com/f/awesome-lists/devtools/data-science-learning.md) — Provides a comprehensive curated roadmap and educational resource for navigating the data science learning path.
- [Hierarchical Organization](https://awesome-repositories.com/f/awesome-lists/productivity/notes-and-knowledge/digital-note-organization/hierarchical-organization.md) — Uses a nested tree structure to organize machine learning concepts from broad to specific.
- [Learning and Reference](https://awesome-repositories.com/f/awesome-lists/ai/learning-and-reference.md) — ML concepts mindmap.
- [Learning Roadmaps and Guides](https://awesome-repositories.com/f/awesome-lists/ai/learning-roadmaps-and-guides.md) — Visual roadmap for learning machine learning concepts.

### Artificial Intelligence & ML

- [Machine Learning Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-implementations.md) — Connects theoretical learning to practical code-based reference examples of core machine learning algorithms.
- [Concept Mindmaps](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-knowledge-bases/concept-mindmaps.md) — Organizes core machine learning concepts into structured visual mindmaps for conceptual study.
- [Conceptual Visualizations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/machine-learning-concepts/conceptual-visualizations.md) — Provides diagrams and visual summaries to illustrate the internal logic of machine learning model architectures. ([source](https://github.com/dformoso/machine-learning-mindmap/blob/master/README.md))

### Content Management & Publishing

- [Markdown-Based Knowledge Bases](https://awesome-repositories.com/f/content-management-publishing/content-management-systems/content-architecture-modeling/markdown-ecosystem-tools/markdown-based-knowledge-bases.md) — Implements a version-controlled knowledge repository built using plain-text markdown to map technical relationships.

### Data & Databases

- [Markdown Knowledge Bases](https://awesome-repositories.com/f/data-databases/file-based-storage-systems/markdown-memory-stores/markdown-knowledge-bases.md) — Uses a system of interconnected markdown notes to map relationships between technical topics.

### Education & Learning Resources

- [Machine Learning Roadmaps](https://awesome-repositories.com/f/education-learning-resources/curricula-instructional-design/curricula-roadmaps/ai-machine-learning-roadmaps/foundational-ml-data-science/machine-learning-roadmaps.md) — Provides a structured roadmap for mastering model training and data processing through linked theory and practice.
- [Runnable Demonstrations](https://awesome-repositories.com/f/education-learning-resources/documentation-examples/runnable-demonstrations.md) — Links theoretical concepts to isolated, executable notebook demonstrations for practical hands-on learning. ([source](https://github.com/dformoso/machine-learning-mindmap#readme))
- [Machine Learning Education](https://awesome-repositories.com/f/education-learning-resources/educational-resources/systems-applied-computing/machine-learning-education.md) — Offers educational materials focused on teaching the fundamental concepts and implementation techniques of machine learning.
- [Machine Learning Learning Paths](https://awesome-repositories.com/f/education-learning-resources/front-end-learning-paths/machine-learning-learning-paths.md) — Presents a structured educational sequence teaching machine learning from basic data analysis to advanced deep learning.
- [Machine Learning Study Paths](https://awesome-repositories.com/f/education-learning-resources/machine-learning-study-paths.md) — Offers structured sequences of learning activities and visual summaries to master data science and algorithms. ([source](https://github.com/dformoso/machine-learning-mindmap#readme))
- [Educational Resource Collections](https://awesome-repositories.com/f/education-learning-resources/educational-resource-collections.md) — Curates a collection of summaries, diagrams, and implementation examples for technical skill acquisition in ML.
- [Technical Concept Diagrams](https://awesome-repositories.com/f/education-learning-resources/learning-platforms-infrastructure/educational-infrastructure/educational-platforms/technical-concept-diagrams.md) — Uses high-density graphical representations to simplify complex relationships between mathematics and model architectures.
- [Modular Learning Paths](https://awesome-repositories.com/f/education-learning-resources/modular-learning-paths.md) — Structures the learning content into independent, non-linear modules for flexible navigation of the domain.